Biological Sequence Analysis

BackgroundMolecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet.ResultsIn this paper, we discuss an efficient and general method to compute the score distribution to any desired accuracy. The general approach may be applied to different sequence models and and various similarity measures that satisfy a few weak assumptions. We have access to the low-probability region ("tail") of the distribution where scores are larger than expected by pure chance and therefore relevant for practical applications. Our method uses recent ideas from rare-event simulations, combining Markov chain Monte Carlo simulations with importance sampling and generalized ensembles. We present results for the score statistics of fixed and random queries against random sequences. In a second step, we extend the approach to a model of transmembrane proteins, which can hardly be described as i.i.d. sequences. For this case, we compare the statistical properties of a fixed query model as well as a hidden Markov sequence model in connection with a position based scoring scheme against the classical approach.ConclusionsThe results illustrate that the sensitivity and specificity strongly depend on the underlying scoring and sequence model. A specific ROC analysis for the case of transmembrane proteins supports our observation.

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“…Note that these probabilities are not normalized, in particular

\sum_{μ \in normalΓ} v_{μ} (i) \leq 1

. The missing normalization is no problem, since we are interested only in the most probable path, which is reconstructed by back-tracking [2]. …”

Section: Methodsmentioning

confidence: 99%

Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling

et al. 2011

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“…The results may change according to the order of sentences to be aligned, and the way the order is determined is not mentioned. We employed a progressive search [12], which could be expected to produce more accurate alignment. The progressive search algorithm is as follows:…”

Section: Sentence Alignmentmentioning

confidence: 99%

Committee-Based Active Learning for Speech Recognition

Hamanaka

Sairyo

Tsutaoka

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a committee-based method of active learning for large vocabulary continuous speech recognition. Multiple recognizers are trained in this approach, and the recognition results obtained from these are used for selecting utterances. Those utterances whose recognition results differ the most among recognizers are selected and transcribed. Progressive alignment and voting entropy are used to measure the degree of disagreement among recognizers on the recognition result. Our method was evaluated by using 191-hour speech data in the Corpus of Spontaneous Japanese. It proved to be significantly better than random selection. It only required 63 h of data to achieve a word accuracy of 74%, while standard training (i.e., random selection) required 103 h of data. It also proved to be significantly better than conventional uncertainty sampling using word posterior probabilities. key words: active learning, query by committee, LVCSR, progressive alignment

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“…We also review the core Viterbi algorithm used to determine whether the motif described by an HMM occurs in a given protein sequence. We assume that the reader has basic familiarity with HMMs; a more detailed description of these models and their use in similarity search may be found in [4].…”

Section: Background: Detecting Motifs Via Hidden Markov Modelsmentioning

confidence: 99%